Deep learning based operational domain verification using camera-based inputs for autonomous systems and applications

ABSTRACT

In various examples, methods and systems are provided for determining, using a machine learning model, one or more of the following operational domain conditions related to an autonomous and/or semi-autonomous machine: amount of camera blindness, blindness classification, illumination level, path surface condition, visibility distance, scene type classification, and distance to a scene. Once one or more of these conditions are determined, an operational level of the machine may be determined, and the machine may be controlled according to the operational level.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Non-Provisional application Ser. No.16/570,187, filed on Sep. 13, 2019, and U.S. Non-Provisional applicationSer. No. 17/449,306, filed on Sep. 29, 2021, each of which is herebyincorporated by reference in their entirety.

BACKGROUND

Autonomous driving systems and semi-autonomous driving systems (e.g.,advanced driver assistance systems (ADAS)) may use sensors (e.g., suchas cameras, LiDAR sensors, RADAR sensors, etc.) in order to makedecisions regarding the performance of various tasks—such as increasing,reducing, or maintaining a certain speed; blind spot monitoring;automatic emergency braking; lane keeping; object detection; obstacleavoidance; lane changing; lane assignment; camera calibration; andlocalization. In order to determine whether or not and to what extentthese autonomous and semi-autonomous systems should be operatingautonomously, an accurate understanding of the impact that thesurrounding environment has on these systems may be beneficial. Asensor's ability to perceive the surrounding environment, however, maybe compromised by a variety of sources—such as weather (e.g., rain, fog,snow, hail, smoke, etc.), traffic conditions, sensor blockage (e.g.,from debris, precipitation, etc.), or blur—which may lead to sensorblindness and/or compromised visibility distance. Potential causes ofsensor blindness and/or compromised visibility distance may includesnow, rain, glare, sun flares, mud, water, signal failure, and the like.

Conventional systems for addressing sensor blindness and compromisedvisibility distance have used feature-level approaches to detectindividual pieces of visual evidence of compromised visibility andsensor blindness, and subsequently pieced these features together todetermine that sensor blindness and/or compromised visibility eventsexist. These conventional methods primarily rely on computer visiontechniques—such as by analyzing the absence of sharp edge features(e.g., sharp changes in gradient, color, intensity) in regions of theimage, using color-based pixel analysis or other low-level featureanalysis to detect potential visibility and sensor blockage issues,and/or binary support vector machine classification with a blind versusnot blind output. However, such feature-based computer vision techniquesrequire separate analysis of each feature—e.g., whether or not eachfeature is relevant to visibility and sensor blindness—as well as ananalysis of how to combine the different features for a specificsensor's reduced visibility or blindness condition, thereby limiting thescalability of such approaches due to the complexity inherent to thelarge variety and diversity of conditions and occurrences that cancompromise data observed using sensors in real-world situations. Forexample, due to the computational expense of executing theseconventional approaches, they may be less effective for real-time ornear real-time deployment.

Further, conventional systems may rely on classifying the causes thatcontribute to reduced sensor visibility distance—such as rain, snow,fog, glare, etc.—but may not provide an accurate indication of theusability of the sensor data. For example, identifying rain in an imagemay not be actionable by the system for determining whether thecorresponding image—or a portion thereof—is usable for variousautonomous or semi-autonomous tasks. In such an example, where rain ispresent, the image may be deemed unusable by conventional systems, eventhough the image may clearly depict the environment within 100 meters ofthe vehicle. As such, instead of relying on the image for one or moretasks within the visible range, the image may be mistakenly discardedand the one or more tasks may be disabled. Conventional systems may alsobe unable to differentiate between different types of sensor blindness,such as whether an image is blurred or occluded. By treating each typeof compromised sensor visibility distance and sensor blindness withequal or generally equivalent weight, less egregious or detrimentaltypes of compromised sensor visibility distance and sensor blindness maycause an instance of sensor data to be deemed unusable even where thisdetermination may not be entirely accurate (e.g., an image of anenvironment where a light drizzle is present may be usable for one ormore operations while an image of an environment with dense fog may not,a blurred image may be usable for some operations while an occludedimage may not).

Moreover, by relying on hard-coded computer vision techniques,conventional systems may be less able to learn from historical data, orto learn over time in deployment, thereby limiting the ability of thesesystems to adapt to new types or occurrences of sensor blindnessconditions. In addition, conventional systems for detecting illuminationlevels and path surface conditions of the surrounding environment mayrely on dedicated sensors, for example, LUX sensors, for measuring lightintensity, and/or may rely on road surface wetness sensors placed nextto tires. These dedicated sensors may output low accuracy and non-richdata of the surrounding environment because of the unavailability ofrich data inputs. As a result, these dedicated sensors may be lesscapable of differentiating between illumination instances (e.g.,illumination caused by a street lamp versus illumination caused byindoor parking garage lighting) and different path surface conditions(e.g., when a road is snowy versus when a road is free of snow but snowhas nonetheless accumulated on a machine's sensor(s)).

SUMMARY

Embodiments of the present disclosure relate to deep learning basedoperational domain verification using camera-based inputs for autonomoussystems and applications. Systems and methods are disclosed that use oneor more machine learning models—such as deep neural networks (DNNs)—tocompute outputs indicative of one or more of the followingoperational-design-domain conditions related to an autonomous and/orsemi-autonomous machine: amount of camera blindness; blindnessclassification corresponding to camera blindness; illumination level;path surface condition; visibility distance; scene type classification;and distance to a scene corresponding to scene type classification.These outputs may then be used to determine the level of autonomy, e.g.,an operational level, for the autonomous and/or semi-autonomous machine,and the machine may then be controlled through an environment using thedetermined autonomy level.

In contrast to conventional systems, such as those described above, thesystems and methods of the present disclosure may acquireoperational-design-domain conditions through sensor data therebyproviding accurate and rich information regarding these conditionsanalogous to human perception. Moreover, because the systems and methodsof the present disclosure apply multi-task deep learning, lesscomputational resources may be required in comparison to conventionalapproaches.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for deep learning based operationaldesign domain verification using camera-based inputs for autonomousmachine applications are described in detail below with reference to theattached drawing figures, wherein:

FIG. 1A is an example data flow diagram illustrating an example processfor determining illumination level, path surface condition, visibilitydistance, scene classification, distance to a scene, and sensorblindness and its cause(s), in accordance with some embodiments of thepresent disclosure;

FIG. 1B is an example machine learning model for implementing an exampleprocess for determining illumination level, path surface condition,visibility distance, scene classification, distance to a scene, andsensor blindness and its cause(s), in accordance with some embodimentsof the present disclosure;

FIGS. 2A-2B include example visualizations of sensor data includingvarying visibility distance levels, in accordance with some embodimentsof the present disclosure;

FIG. 3 includes example visualizations of sensor data including varyingsensor blindness types, in accordance with some embodiments of thepresent disclosure;

FIGS. 4A-4B include example visualizations of sensor data includingvarying scene types, in accordance with some embodiments of the presentdisclosure;

FIG. 5 is a flow diagram illustrating a method for using a neuralnetwork to determine illumination level, path surface condition,visibility distance, scene classification, distance to a scene, andsensor blindness and its cause(s), in accordance with some embodimentsof the present disclosure;

FIG. 6A is an illustration of an example autonomous vehicle inaccordance with some embodiments of the present disclosure;

FIG. 6B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 6A in accordance with someembodiments of the present disclosure;

FIG. 6C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 6A, in accordance with someembodiments of the present disclosure;

FIG. 6D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 6A, in accordancewith some embodiments of the present disclosure;

FIG. 7 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure; and

FIG. 8 is a block diagram of an example data center suitable for use inimplementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to deep learning basedoperational domain verification using camera-based inputs for autonomoussystems and applications. The systems and methods described herein maybe used in augmented reality, virtual reality, mixed reality, robotics,security and surveillance, medical imaging, autonomous orsemi-autonomous machine applications, and/or any other technology spaceswhere determining illumination level, path surface condition, visibilitydistance, scene classification, distance to a scene, and sensorblindness and its cause(s) may be implemented. Although the presentdisclosure may be described with respect to an example autonomousvehicle 600 (alternatively referred to herein as “vehicle 600” or“ego-machine 600,” an example of which is described with respect toFIGS. 6A-6D), this is not intended to be limiting. For example, thesystems and methods described herein may be used by, without limitation,non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or moreadvanced driver assistance systems (ADAS)), piloted and un-pilotedrobots or robotic platforms, warehouse vehicles, off-road vehicles,vehicles coupled to one or more trailers, flying vessels, boats,shuttles, emergency response vehicles, motorcycles, electric ormotorized bicycles, aircraft, construction vehicles, underwater craft,drones, and/or other vehicle types.

In disclosed approaches, a processor comprising one or more circuits maycompute data representative of operational domain conditions using aneural network and based at least in part on image data generated usingone or more image sensors. In certain embodiments, operational domainconditions may include one or more of the following: an amount of camerablindness, a blindness classification corresponding to the amount ofcamera blindness, an illumination level, a path surface condition, avisibility distance, a scene type classification, and a distance to ascene corresponding to the scene type classification.

Deep neural network (DNN) processing may be used to determine an amountof camera blindness, a blindness classification corresponding to theamount of camera blindness, an illumination level, a path surfacecondition, a visibility distance, a scene type classification, and adistance to a scene corresponding to the scene type classification.Sensor data (e.g., representative of image(s)) may be received from oneor more sensors (e.g., cameras) disposed on or otherwise associated witha vehicle. The sensor data may be applied to a neural network (e.g., aDNN, such as a convolutional neural network (CNN)) that may be trainedto determine the amount of camera blindness, an illumination level, apath surface condition, a visibility distance, a scene typeclassification, and a distance to a scene corresponding to the scenetype classification associated with the sensor data. Methods and systemsfor determining an amount of camera blindness, a blindnessclassification corresponding to the amount of camera blindness, and avisibility distance may be the same or similar to those methods andsystems described in U.S. Non-Provisional application Ser. No.16/570,187, filed on Sep. 13, 2019, and U.S. Non-Provisional applicationSer. No. 17/449,306, filed on Sep. 29, 2021, which are herebyincorporated by reference in their entirety.

Based at least in part on this sensor data, an operational level of theego-machine that is appropriate to the current domain or environment maybe determined. In embodiments, an appropriate operational level maycorrespond with one or more of the automation levels defined by theSociety of Automotive Engineers (SAE), such as Level 0 (no automation),Level 1 (driver assistance), Level 2 (partial automation), Level 3(conditional automation), Level 4 (high automation), and Level 5 (fullautomation).

Disclosed embodiments may be comprised in a variety of different systemssuch as automotive systems (e.g., a control system for an autonomous orsemi-autonomous machine, a perception system for an autonomous orsemi-autonomous machine), systems implemented using a robot, aerialsystems, medial systems, boating systems, smart area monitoring systems,systems for performing deep learning operations, systems for performingsimulation operations, systems implemented using an edge device, systemsincorporating one or more virtual machines (VMs), systems implemented atleast partially in a data center, systems implemented at least partiallyusing cloud computing resources, and/or other types of systems. Whilespecific examples are provided, these example may be generalized beyondimplementations details.

Now referring to FIG. 1A, FIG. 1A is an example data flow diagramillustrating an example process 100 for determining illumination level,path surface condition, visibility distance, scene classification,distance to a scene, and sensor blindness and its cause(s), inaccordance with some embodiments of the present disclosure. It should beunderstood that this and other arrangements described herein are setforth only as examples. Other arrangements and elements (e.g., machines,interfaces, functions, orders, groupings of functions, etc.) may be usedin addition to or instead of those shown, and some elements may beomitted altogether. Further, many of the elements described herein arefunctional entities that may be implemented as discrete or distributedcomponents or in conjunction with other components, and in any suitablecombination and location. Various functions described herein as beingperformed by entities may be carried out by hardware, firmware, and/orsoftware. For instance, various functions may be carried out by aprocessor executing instructions stored in memory. In some embodiments,the systems, methods, and processes described herein may be executedusing similar components, features, and/or functionality to those ofexample autonomous vehicle 600 of FIGS. 6A-6D, example computing device700 of FIG. 7 , and/or example data center 800 of FIG. 8 .

The process 100 may include generating and/or receiving sensor data 102from one or more sensors of the ego-machine 600. The sensor data 102 maybe used by the ego-machine 600, and within the process 100, to determinein real-time or near real-time one or more of the following: an amountof camera blindness, a blindness classification corresponding to theamount of camera blindness, an illumination level, a path surfacecondition, a visibility distance, a scene type classification, and adistance to a scene corresponding to the scene type classification. Thesensor data 102 may include, without limitation, sensor data 102 fromany of the sensors of the ego-machine 600 (and/or other vehicles orobjects, such as robotic devices, VR systems, AR systems, etc., in someexamples). For example, and with reference to FIGS. 6A-6C, the sensordata 102 may include the data generated by, without limitation, globalnavigation satellite systems (GNSS) sensor(s) 658 (e.g., GlobalPositioning System sensor(s)), RADAR sensor(s) 660, ultrasonic sensor(s)662, LIDAR sensor(s) 664, inertial measurement unit (IMU) sensor(s) 666(e.g., accelerometer(s), gyroscope(s), magnetic compass(es),magnetometer(s), etc.), microphone(s) 676, stereo camera(s) 668,wide-view camera(s) 670 (e.g., fisheye cameras), infrared camera(s) 672,surround camera(s) 674 (e.g., 360 degree cameras), long-range and/ormid-range camera(s) 678, speed sensor(s) 644 (e.g., for measuring thespeed of the vehicle 600), and/or other sensor types. As anotherexample, the sensor data 102 may include virtual sensor data generatedfrom any number of sensors of a virtual vehicle or other virtual object.In such an example, the virtual sensors may correspond to a virtualvehicle or other virtual object in a simulated environment (e.g., usedfor testing, training, and/or validating neural network performance),and the virtual sensor data may represent sensor data captured by thevirtual sensors within the simulated or virtual environment. As such, byusing the virtual sensor data, the machine learning model(s) 104described herein may be tested, trained, and/or validated usingsimulated data in a simulated environment, which may allow for testingmore extreme scenarios outside of a real-world environment where suchtests may be less safe.

The sensor data 102 may include image data representing an image(s),image data representing a video (e.g., snapshots of video), and/orsensor data representing representations of sensory fields of sensors(e.g., depth maps for LIDAR sensors, a value graph for ultrasonicsensors, etc.). Where the sensor data 102 includes image data, any typeof image data format may be used, such as, for example and withoutlimitation, compressed images such as in Joint Photographic ExpertsGroup (JPEG) or Luminance/Chrominance (YUV) formats, compressed imagesas frames stemming from a compressed video format such as H.264/AdvancedVideo Coding (AVC) or H.265/High Efficiency Video Coding (HEVC), rawimages such as originating from Red Clear Blue (RCCB), Red Clear (RCCC),or other type of imaging sensor, and/or other formats. In addition, insome examples, the sensor data 102 may be used within the process 100without any pre-processing (e.g., in a raw or captured format), while inother examples, the sensor data 102 may undergo pre-processing (e.g.,noise balancing, demosaicing, scaling, cropping, augmentation, whitebalancing, tone curve adjustment, etc., such as using a sensor datapre-processor (not shown)). As used herein, the sensor data 102 mayreference unprocessed sensor data, pre-processed sensor data, or acombination thereof.

The sensor data pre-processor may use image data representative of oneor more images (or other data representations) and load the sensor datainto memory in the form of a multi-dimensional array/matrix(alternatively referred to as tensor, or more specifically an inputtensor, in some examples). The array size may be computed and/orrepresented as W×H×C, where W stands for the image width in pixels, Hstands for the height in pixels, and C stands for the number of colorchannels. Without loss of generality, other types and orderings of inputimage components are also possible. Additionally, the batch size B maybe used as a dimension (e.g., an additional fourth dimension) whenbatching is used. Batching may be used for training and/or forinference. Thus, the input tensor may represent an array of dimensionW×H×C×B. Any ordering of the dimensions may be possible, which maydepend on the particular hardware and software used to implement thesensor data pre-processor. This ordering may be chosen to maximizetraining and/or inference performance of the machine learning model(s)104.

In some embodiments, a pre-processing image pipeline may be employed bythe sensor data pre-processor to process a raw image(s) acquired by asensor(s) (e.g., camera(s)) and included in the image data 102 toproduce pre-processed image data which may represent an input image(s)to the input layer(s) of the machine learning model(s) 104. An exampleof a suitable pre-processing image pipeline may use a raw RCCB Bayer(e.g., 1-channel) type of image from the sensor and convert that imageto a RCB (e.g., 3-channel) planar image stored in Fixed Precision (e.g.,16-bit-per-channel) format. The pre-processing image pipeline mayinclude decompanding, noise reduction, demosaicing, white balancing,histogram computing, and/or adaptive global tone mapping (e.g., in thatorder, or in an alternative order).

Where noise reduction is employed by the sensor data pre-processor, itmay include bilateral denoising in the Bayer domain. Where demosaicingis employed by the sensor data pre-processor, it may include bilinearinterpolation. Where histogram computing is employed by the sensor datapre-processor, it may involve computing a histogram for the C channel,and may be merged with the decompanding or noise reduction in someexamples. Where adaptive global tone mapping is employed by the sensordata pre-processor, it may include performing an adaptive gamma-logtransform. This may include calculating a histogram, getting a mid-tonelevel, and/or estimating a maximum luminance with the mid-tone level.

The machine learning model(s) 104 may use as input one or more images orother data representations (e.g., LIDAR point clouds, RADARrepresentations, etc.) as represented by the sensor data 102 to generateoutput(s) 106. In a non-limiting example, the machine learning model(s)104 may take, as input, an image(s) represented by the sensor data 102(e.g., after pre-processing) to generate the output(s) 106, e.g., camerablindness 108, blindness classification(s) 110, illumination level 112,path surface condition 114, visibility distance, scene-typeclassification 118, and/or distance to a scene 120. Although examplesare described herein with respect to using neural networks, andspecifically DNNs, as the machine learning model(s) 104, this is notintended to be limiting. For example, and without limitation, themachine learning model(s) 104 described herein may include any type ofmachine learning model, such as a machine learning model(s) using linearregression, logistic regression, decision trees, support vector machines(SVM), Naïve Bayes, k-nearest neighbor (Knn), K means clustering, randomforest, dimensionality reduction algorithms, gradient boostingalgorithms, neural networks (e.g., auto-encoders, convolutional,recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield,Boltzmann, deep belief, deconvolutional, generative adversarial, liquidstate machine, etc.), and/or other types of machine learning models.

With reference to computing the camera blindness 108 and the blindnessclassification 110, the machine learning model(s) 104 may identify areasof interest within the sensor data 102 pertaining to sensor blindness,as well as to identify causes thereof (e.g., blurred, blocked, etc.).More specifically, the machine learning model(s) 104 may be designed toinfer blindness markers and output classifications that identify wherein the sensor data the potential sensor blindness may be located, acause of the sensor blindness, and whether the sensor data, or a portionthereof, is usable by the system. In some examples, the neural networkmay output a binary decision (e.g., True/False, Yes/No, 0/1) indicatingthat the sensor data is at least partially usable (e.g., true, yes, 0)and a second decision indicating that the sensor data is not usable(e.g., false, no, 1). Where the data is indicated as being not usable,the sensor data may be skipped over, disregarded, and/or used to make adetermination to reduce the operational level to Level 0, e.g. to handcontrol back to a driver in autonomous or semi-autonomous applications.

The blindness classification 110 may represent the cause for blindnessassociated with one or more pixels of the sensor data 101 with detectedblindness. The sensor data 102 may include any number of differentblindness classifications. For example, a single blindness region(s) orpixel may have one, two, or more associated blindness classificationswithout departing from the scope of the present disclosure. In someexamples, the machine learning model(s) 104 may output a number ofchannels corresponding to a number of classifications the machinelearning model(s) is trained to predict (e.g., each channel maycorrespond to one blindness classification). For example, the blindnessclassification 110 may include one or more of blocked, blurred,reflection, open, ego-vehicle, sky, frame label, etc., and the number ofoutput channels may correspond to the number of classifications desired.The blindness classification 110 may include sub-classifications,including but not limited to, rain, glare, broken lens, light, mud,paper, person, etc.

In examples, a blindness classification of “blocked” may be associatedwith one or more blindness sub-classifications, such as sun, fog, water,mist, snow, frozen pane, day, night, broken lens, self-glare, mud,paper, leaf, etc. In this way, blindness sub-classifications may furtherdelineate a cause for the blindness in the sensor data 102. In someexamples, the blindness sub-classifications may include furthersub-classification blindness causes, such as to indicate a level ofcompromised visibility (e.g., heavy, moderate, light, etc.). The machinelearning model(s) 104 may similarly have as many output channels as thenumber of blindness classifications and/or corresponding blindnesssub-classifications that the machine learning model(s) is trained topredict.

The machine learning model(s) 104 may be trained to predict thevisibility distance 116 for the sensor data 102, e.g., a furthestdistance from a sensor that objects or elements may be discerned. Anoperational level of the machine may be adjusted based on the predictedvisibility distance. For example, where the machine learning model(s)104 determines the visibility distance is low—e.g., 20 meters orless—the corresponding sensor data may only be relied upon for Level 0(no automation) or Level 1 (driver assistance) tasks, or may be reliedupon only for predictions that are within 20 meters of the machine(e.g., and predictions beyond 20 meters may be disregarded, or more havea lower associated confidence). Similarly, as another example, where anestimated visibility distance is high—e.g., 1000 meters or more—thecorresponding sensor data may be relied upon for the full performance ofLevel 3 (conditional driving automation) and Level 4 (high drivingautomation) tasks, or may be relied upon for predictions correspondingto locations within 1000 meters of the machine.

In embodiments, the computed visibility distance may be categorized asvery low visibility (e.g., between 10 meters and 100 meters), lowvisibility (e.g., between 10 meters and 100 meters), medium visibility(e.g., between 100 meters and 1000 meters), high visibility (e.g.,between 1000 meters and 4000 meters), or clear visibility (e.g., greaterthan 4000 meters). In other embodiments, very low visibility may becorrelated to extreme fog or blowing snow, or high precipitation orheavy snow; low visibility may be correlated to dense fog weather;medium visibility may be correlated to moderate fog weather or mediumprecipitation or moderate drizzle; high visibility may be correlated toa hazy weather condition or light precipitation or drizzle; and clearvisibility may be correlated to a maximum visual range of the particularsensor(s) generating the sensor data.

A predicted visibility distance may correspond to various tasks that theego-machine may perform. For example, for very low visibility distance,the full performance of Level 0, Level 1, and Level 2 low speed activesafety functions (e.g., lane assist, automatic lane keep, automaticcruise control, automatic emergency braking, etc.) may be available aslong as the ego-machine is traveling at less than a threshold speed(e.g., less than 25 kilometers per hour). However, if the vehicle istraveling beyond the threshold speed, these functionalities may bedisabled—at least with respect to the instance(s) of sensor data with avery low disability distance. With respect to low visibility distance,the full performance of the Level 0, Level 1, and Level 2 parkingfunctions (e.g., proximity detection, automatic parallel parkingalignment, etc.) and low speed active safety functions may be availableso long as the ego-machine is traveling at less than a threshold speed.With respect to medium visibility distance, the full performance of theLevel 0, Level 1, Level 2 and Level 2+ driving functions may beavailable. With respect to high visibility distance, the fullperformance of Level 3 and Level 4 parking functions (e.g., automaticparallel parking (APP), MPP, VVP) may be available, in addition to theLevel 0, Level 1, Level 2, and Level 2+ functions of the first, second,and third bin. With respect to clear visibility, the full performance ofLevel 3 automated driving highway function may be available, in additionto the Level 0, Level 1, Level 2, and Level 2+ functions.

The machine learning model(s) 104 may also be trained to predict thescene type classification 118 for the sensor data 102 and the distanceto the scene corresponding to the scene type classification 120. Forexample, the machine learning model(s) 104 may be trained to predictthat sensor data represents one or more of a tunnel, a constructionzone, a blocked lane, or a toll plaza as well as the distance to thescene type, e.g., the affected road area. The machine learning model(s)104 may further be trained to predict the path surface condition 114from the sensor data 102, such as dry, wet, snowy, icy, etc., as well asthe illumination level 112, e.g. the amount of light perceived by theroad user.

In embodiments, such as where the machine learning model(s) 104 includesa DNN, the machine learning model(s) 104 may include any number oflayers. One or more of the layers may include an input layer. The inputlayer may hold values associated with the sensor data 402. One or morelayers may include convolutional layers. The convolutional layers maycompute the output of neurons that are connected to local regions in aninput layer, each neuron computing a dot product between their weightsand a small region they are connected to in the input volume. A resultof the convolutional layers may be another volume, with one of thedimensions based on the number of filters applied (e.g., the width, theheight, and the number of filters, such as 32×32×12, if 12 were thenumber of filters).

One or more layers may include deconvolutional layers (or transposedconvolutional layers). For example, a result of the deconvolutionallayers may be another volume, with a higher dimensionality than theinput dimensionality of data received at the deconvolutional layer.

One or more of the layers may include a rectified linear unit (ReLU)layer. The ReLU layer(s) may apply an elementwise activation function,such as the max (0, x), thresholding at zero, for example. The resultingvolume of a ReLU layer may be the same as the volume of the input of theReLU layer.

One or more of the layers may include a pooling layer. The pooling layermay perform a down sampling operation along the spatial dimensions(e.g., the height and the width), which may result in a smaller volumethan the input of the pooling layer (e.g., 16×16×12 from the 32×32×12input volume).

One or more of the layers may include one or more fully connectedlayer(s). Each neuron in the fully connected layer(s) may be connectedto each of the neurons in the previous volume. The fully connected layermay compute class scores, and the resulting volume may be 1×1×n, where nis equivalent to the number of classes. In some examples, the CNN mayinclude a fully connected layer(s) such that the output of one or moreof the layers of the CNN may be provided as input to a fully connectedlayer(s) of the CNN. In some examples, one or more convolutional streamsmay be implemented by the machine learning model(s) 104, and some or allof the convolutional streams may include a respective fully connectedlayer(s).

In some non-limiting embodiments, the machine learning model(s) 104 mayinclude a series of convolutional and max pooling layers to facilitateimage feature extraction, followed by multi-scale dilated convolutionaland up-sampling layers to facilitate global context feature extraction.

Although input layers, convolutional layers, pooling layers, ReLUlayers, and fully connected layers are discussed herein with respect tothe machine learning model(s) 104, this is not intended to be limiting.For example, additional or alternative layers may be used in the machinelearning model(s) 104, such as normalization layers, SoftMax layers,and/or other layer types.

In addition, some of the layers may include parameters (e.g., weightsand/or biases), such as the convolutional layers and the fully connectedlayers, while others may not, such as the ReLU layers and poolinglayers. In some examples, the parameters may be learned by the machinelearning model(s) 104 during training. Further, some of the layers mayinclude additional hyper-parameters (e.g., learning rate, stride,epochs, etc.), such as the convolutional layers, the fully connectedlayers, and the pooling layers, while other layers may not, such as theReLU layers. In embodiments where the machine learning model(s) 104regress on visibility distances, the activation function of a last layerof the CNN may include a ReLU activation function. In embodiments wherethe machine learning model(s) 104 classifies instances of sensor datainto a distance bin, the activation function of a last layer of the CNNmay include a SoftMax activation function. The parameters andhyper-parameters are not to be limited and may differ depending on theembodiment.

In embodiments where the machine learning model(s) 104 includes a CNN,different orders and numbers of the layers of the CNN may be useddepending on the embodiment. In other words, the order and number oflayers of the CNN is not limited to any one architecture.

In embodiments, determining an operational level of the ego-machine,e.g. operations 124, may include weighting the camera blindness, theblindness classification, the illumination level, the path surfacecondition, the visibility distance, the scene type classification, andthe distance to a scene according to pre-defined weights. In otherembodiments, the camera blindness may be associated with a highestweight of the pre-defined weights.

Referring now to FIG. 1B with FIG. 1A, FIG. 1B is an example machinelearning model(s) 104A for implementing an example process fordetermining illumination level, path surface condition, visibilitydistance, scene classification, distance to a scene, and/or sensorblindness and/or its cause(s), in accordance with some embodiments ofthe present disclosure. For example, in one or more embodiments, themachine learning model(s) 104A may include a CNN that may include one ormore trunks, an encoder decoder architecture, and/or one or more outputheads (e.g. a blindness mask head 142, an illumination head 144, a pathsurface head 146, a blindness cause head 148, and a scene classificationhead 150). In examples, the CNN may include one or more convolutionallayers corresponding to a feature detection trunk, where the featuredetection trunk may output intermediate data (e.g., representative offeature maps), which may be processed using the one or more outputheads. For example, using the intermediate data, a first output head,e.g. the blindness mask head 142, may be used to compute an amount ofcamera blindness, a second output head, e.g. the blindness cause head148, may be used to compute a camera blindness classification, a thirdoutput head, e.g. the illumination head 144, may be used to compute anillumination level, a fourth output head, e.g. the path surface head146, may be used to compute a path surface condition, and/or a fifthoutput head, e.g. the scene head 150, may be used to compute a scenetype classification (e.g. a probability of a type of scene 152) and/or adistance to the scene corresponding to the scene type classification(e.g. a distance to a scene 154). In other embodiments, the fifth outputhead may be used to compute the scene type classification, and a sixthoutput head may be used to compute the distance to the scene. Where twoor more heads are used, the two or more heads may process data from thetrunk in parallel, and each head may be trained to accurately predictthe corresponding output(s) of that output head. In other embodiments,however, a single trunk may be used, without separate heads.

In examples, one or more of the first, second, third, fourth, fifth,and/or sixth heads of the CNN of the machine learning model(s) 104 mayinclude at least one fully connected layer. For example, theillumination head 144, the path surface head 146, the blindness causehead 148, and the scene head 150, may each include at least one fullyconnected layer. Each neuron in the fully connected layer(s) may beconnected to each of the neurons in the previous volume. The fullyconnected layer may compute class scores, and the resulting volume maybe 1×1×n, where n is equivalent to the number of classes. In somenon-limiting embodiments, one or more of the heads of the CNN of themachine learning model(s) 104 may include one or more down-samplinglayers and one or more up-sampling layers. For example, the blindnessmask head 142 of the CNN of the machine learning model(s) 104 mayinclude a series of down-sampling layers (e.g., pooling layers) tofacilitate image feature extraction followed by up-sampling layers tofacilitate global context feature extraction. In embodiments, one ormore pooling layers may perform a down sampling operation along thespatial dimensions (e.g., the height and the width), which may result ina smaller volume than the input of the pooling layer (e.g., 16×16×12from the 32×32×12 input volume). In examples, each of the illuminationhead 144, the path surface head 146, the blindness cause head 148, andthe scene head 150, may include a pooling layer. In further examples,the scene head 150 may output the probability of a type of scene 152,such as a tunnel, a construction zone, a blocked lane, or a toll plaza,using a sigmoid function. In other examples, the scene head 150 may alsooutput the distance to a scene 154 using a ReLU layer.

Referring back to FIG. 1A, in some embodiments, after being computed bythe machine learning model(s) 104, the outputs 106 may undergopost-processing by a post processor 122. The post processor 122 maydetermine drivability corresponding to the sensor data 102. For example,the post processor 122 may perform temporal smoothing of the outputs 106of the machine learning model(s) 104. Temporal smoothing may be used insome embodiments to improve stability of the system by reducing falsepositives based on a single frame—by incorporating prior predictions ofthe machine learning model(s) 104 corresponding to temporally adjacentframe—to smooth and reduce noise in the output of the machine learningmodel(s) 104. In some examples, values computed by the machine learningmodel(s) 104 for a current instance of the sensor data 102 may beweighed against values computed by the machine learning model(s) 104 forone or more prior instances of the sensor data 102. Where the sensordata 102 is image data representative of images, for example, theoutputs 106 computed by the machine learning model(s) 104 for a currentor most recent image may be weighed against the outputs 106 computed bythe machine learning model(s) 104 for one or more temporallyadjacent—e.g., previous and/or sequential—images. As such, final values(e.g., for the camera blindness 108, the blindness classification(s)110, the illumination level 112, the path surface condition 114, thevisibility distance 116, the scene type classification 118, and thedistance to the scene 120) corresponding to an instance of the sensordata 102 may be determined by weighting prior values associated with oneor more other instances of the sensor data 102 against current valuesassociated with the instance of the sensor data 102. The results of thetemporal smoothing may be analyzed to determine drivability with respectto the sensor data 102.

The operation(s) 124 may be decisions made by the system in real-time ornear real-time using the sensor data 102 and based on the outputs 106.For example, where not clear enough, or useful, some or all of thesensor data 102 may be skipped over or disregarded with respect to oneor more of the control decisions 124. In some examples, such as wherethe sensor data 102 is unusable for safe operation of the vehicle 600,the operation(s) 124 may include handing control back to a driver (e.g.,exiting autonomous or semi-autonomous operation), or executing anemergency or safety maneuver (e.g., coming to a stop, pulling to theside of the road, or a combination thereof). As such, the operation(s)124 may include suggesting one or more corrective measures for effectiveand safe driving—such as ignoring certain instances of the sensor data.

In any example, and with respect to autonomous or semi-autonomousdriving, the operation(s) 124 may include any decision corresponding toa drive stack 126 (e.g., a sensor data manager layer of an autonomousdriving software stack), a perception layer of the drive stack 126, aworld model management layer of the drive stack 126, a planning layer ofthe drive stack 126, a control layer of the drive stack 126, an obstacleavoidance layer of the drive stack 126, and/or an actuation layer of thedrive stack 126. As such, as described herein, the drivability of thesensor data 102 may be separately determined for any number of differentoperations corresponding to one or more layers of the drive stack 126.

As an example, a first drivability may be determined for objectdetection operations with respect to the perception layer of the drivestack 126, and a second drivability may be determined for path planningwith respect to the planning layer of the drive stack 126. Informationrepresentative of the sensor data 102 may be transmitted to the drivestack 126 with an indication that one or more features, functionalities,and/or components of the drive stack may be disabled, enabled, or remainunchanged. The drive stack 126 may include one or more layers, such as aworld state manager layer that manages the world state using one or moremaps (e.g., 3D maps), localization component(s), perceptioncomponent(s), and/or the like. In addition, the autonomous drivingsoftware stack may include planning component(s) (e.g., as part of aplanning layer), control component(s) (e.g., as part of a controllayer), actuation component(s) (e.g., as part of an actuation layer),obstacle avoidance component(s) (e.g., as part of an obstacle avoidancelayer), and/or other component(s) (e.g., as part of one or moreadditional or alternative layers). As such, the operation(s) 124 mayprovide an indication to any of the downstream tasks of the drive stack126 that rely on the sensor data 102 such that appropriate use of thesensor data 102 is managed.

Referring back to FIG. 1A, in embodiments, the machine learning model(s)104 may be trained both with information supplied by the sensor data 102and by information supplied by a training engine 128. In examples, thetraining engine 128 may include ground truth data 130. In someembodiments, the ground truth data 130 may include labels or annotationscorresponding to the sensor data 102. For example, for each instance ofthe sensor data 102A, the labels or annotations may correspond to one ormore of the amount of camera blindness, blindness classificationcorresponding to camera blindness, illumination level, path surfacecondition, visibility distance, scene type classification, and distanceto a scene corresponding to scene type classification. The labels andannotations may be generated within a drawing program (e.g., anannotation program), a computer aided design (CAD) program, a labelingprogram, another type of program suitable for generating theannotations, and/or may be hand drawn, in some examples. In any example,the ground truth data 130 may be synthetically produced (e.g., generatedfrom computer models or renderings), real produced (e.g., designed andproduced from real-world data), machine-automated (e.g., using featureanalysis and learning to extract features from data and then generatelabels), human annotated (e.g., labeler, or annotation expert, definesthe location of the labels), and/or a combination thereof (e.g., humanidentifies an object that is visible, computer determines distance toobject and corresponding visibility distance).

In some embodiments, the determination of the ground truth label fordistance to a scene may be performed manually. In examples, a framewhere a vehicle is in the scene, such as where the vehicle is in atunnel may be selected. Iterative frames coming after this may be markedwith a distance 0 until the scene is no longer visible. Iterative framescoming before the scene may be marked with a distance 0 until theentrance of the scene is found or until the scene is no longer visible.IMU and/or GPS data may then be used to compute the distance between theprevious frame and the current frame. That distance may be added to thedistance of the previous frame to set the distance of the current frame,such that the length to the scene may be determined.

In embodiments, the training engine 128 may include a loss function(s)132 to compare the outputs 106 to the ground truth data 130 (e.g.,corresponding to one or more of the amount of camera blindness,blindness classification corresponding to camera blindness, illuminationlevel, path surface condition, visibility distance, scene typeclassification, and distance to a scene corresponding to scene typeclassification). In embodiments, the machine learning model(s) 104 maybe trained with sensor data 102 using multiple iterations until thevalue of the loss function(s) 132 is below a predetermined threshold.Any type of loss function may be used, such as cross entropy loss, meansquared error, mean absolute error, mean bias error, and/or other lossfunction types. In some examples, gradients of the loss function 132 maybe iteratively computed with respect to training parameters. Anoptimizer, such as an Adam optimizer, stochastic gradient descent, orany other type of optimization algorithm may be used to optimize theloss function 132 while training the machine learning model(s) 104. Themachine learning model(s) 104 may be trained iteratively until thetraining parameters converge to optimum, desired, or accepted values.

Now referring to FIGS. 2A-2B, FIGS. 2A-2B are example visualizations ofsensor data representations of varying visibility distances, inaccordance with some embodiments of the present disclosure. With respectto FIG. 2A, assuming visualization 200A (including a heavy rain)corresponds to an instance of the sensor data 102A (e.g., an image froma camera of a data collection vehicle), a depth value(s) may be knownfor vehicle 202A based on an output from a depth sensor (e.g., LiDAR,RADAR, etc.), an output from a machine learning model or neural network,an output from a computer vision algorithms, and/or another output type.In such an example, as the data collection vehicle was generating theinstance of the sensor data 102A including the vehicle 202A, the datacollection vehicle may have been running one or more underlyingprocesses for determining depth or distance information to objects inthe environment. As such, if the vehicle 202A was the furthest visibleobject from the data collection vehicle, and the depth to the vehicle202A is known, the visibility distance for the instance of the sensordata 102A may correspond to the depth or distance to the vehicle 202A(e.g., plus an addition distance where the environment is visible beyondthe vehicle 202A, or less some distance where the vehicle 202A isslightly visible, or blurred, in embodiments). Similarly, forvisualization 200B (including light rain conditions), vehicle 202B maybe visible, and thus the visibility distance may be determined usingdepth or distance values corresponding to the vehicle 202B. Althoughrain is illustrated and described with respect to FIGS. 2A-2B, this isnot intended to be limiting, and visibility distance in other conditionssuch as fog, snow, sleet, hail, lighting, occlusion, a combinationthereof, etc. may be determined in accordance with embodiments of thepresent disclosure.

Now referring to FIG. 3 , FIG. 3 includes example visualizations ofsensor data with varying levels of sensor blindness, in accordance withsome embodiments of the present disclosure. For example, the machinelearning model(s) 104 may use image data representative of input image310A as input and may output an image mask 310B including the imageblindness regions 312 and 314. In one or more embodiments, the upperportions of the image 310A may be obscured or blurred by precipitation(as depicted in FIG. 3 ), debris, or glare or reflections from sunlight.The different classifications may be represented by different RGB colorsor indicators. The blindness region 312 may be classified as a blockedor blurred area, and the blindness region 314 may also be classified asa blocked or blurred area. The pixels in blindness regions 312 and 314may be similarly classified as blocked or blurred pixels. Visualization310C illustrates image 310A with the image mask 310B overlaid. In thisway, sensor blindness may be accurately determined and visualized in aregion-specific manner. For example, with respect to the image 310A, thedetermination of drivability may be high, such that the image 310A isuseful for autonomous or semi-autonomous driving operations. This may bebecause only a portion of the image 310A is blurred, and the portion isin a sky of the environment where the operation(s) 124 may not beaffected.

Now referring to FIGS. 4A-4B, FIGS. 4A-4B include example visualizationsof illustrations of sensor data corresponding to various scene types, inaccordance with some embodiments of the present disclosure. Asillustrated in FIG. 4A, using image data, such as the sensor data 102and/or data supplied by the training engine 128, the machine learningmodel(s) 104 may output data representative of a scene, such as atunnel. As illustrated in FIG. 4B, using image data, such as the sensordata 102 and/or data supplied by the training engine 128, the machinelearning model(s) 104 may output data representative of a scene, such asa toll plaza.

Now referring to FIG. 5 , each block of method 500, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 500 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 500 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,method 500 is described, by way of example, with respect to theego-machine 600 of FIGS. 6A-6D. However, this method 500 mayadditionally or alternatively be executed within any one process and/orby any one system, or any combination of processes and systems,including, but not limited to, those described herein.

FIG. 5 is a flow diagram illustrating a method 500 for using a neuralnetwork to determine illumination level, path surface condition,visibility distance, scene classification, distance to a scene, andsensor blindness and its cause(s), in accordance with some embodimentsof the present disclosure.

The method 500, at block B502, includes computing, using a neuralnetwork and based at least on image data of an environment generatedusing one or more image sensors, data representative of one or moreparameters of an operational domain corresponding to the environment. Inexamples, the one or more parameters comprise at least one of a camerablindness level, a blindness classification corresponding to the camerablindness, an illumination level, a path surface condition, a visibilitydistance, a scene type classification, or a distance to a scenecorresponding to the scene type classification. In further examples, theneural network may compute the data representative of camera blindness,a blindness classification corresponding to the camera blindness, anillumination level, a path surface condition, a visibility distance, ascene type classification, and a distance to a scene corresponding tothe scene type classification using the sensor data 102 and/or theground truth data 130 and/or the loss function 132.

The method 500, at block B504, includes determining, based at least inpart on the data, an operational level of an ego-machine correspondingto the environment. For example, the operational level may be determinedbased on one or more of the camera blindness, a blindness classificationcorresponding to the camera blindness, an illumination level, a pathsurface condition, a visibility distance, a scene type classification,and/or a distance to a scene corresponding to the scene typeclassification.

The method 500, at block B506, includes controlling an operation of theego-machine according to the operational level. For example, where thescene type classification is determined to be a toll plaza and/or thedistance to the scene is determined to be 100 yards, the ego-machine maydecelerate, in accordance with embodiments of the present disclosure. Inother examples, where the visibility distance is determined to be low,the ego-machine may decelerate or perform other safety maneuvers(stopping, navigating to a safe or designated area, etc.).

Example Autonomous Vehicle

FIG. 6A is an illustration of an example autonomous vehicle 600, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 600 (alternatively referred to herein as the “vehicle600”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,a vehicle coupled to a trailer, and/or another type of vehicle (e.g.,that is unmanned and/or that accommodates one or more passengers).Autonomous vehicles are generally described in terms of automationlevels, defined by the National Highway Traffic Safety Administration(NHTSA), a division of the US Department of Transportation, and theSociety of Automotive Engineers (SAE) “Taxonomy and Definitions forTerms Related to Driving Automation Systems for On-Road Motor Vehicles”(Standard No. J3016-201806, published on Jun. 15, 2018, Standard No.J3016-201609, published on Sep. 30, 2016, and previous and futureversions of this standard). The vehicle 600 may be capable offunctionality in accordance with one or more of Level 3-Level 5 of theautonomous driving levels. The vehicle 600 may be capable offunctionality in accordance with one or more of Level 1-Level 5 of theautonomous driving levels. For example, the vehicle 600 may be capableof driver assistance (Level 1), partial automation (Level 2),conditional automation (Level 3), high automation (Level 4), and/or fullautomation (Level 5), depending on the embodiment. The term“autonomous,” as used herein, may include any and/or all types ofautonomy for the vehicle 600 or other machine, such as being fullyautonomous, being highly autonomous, being conditionally autonomous,being partially autonomous, providing assistive autonomy, beingsemi-autonomous, being primarily autonomous, or other designation.

The vehicle 600 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 600 may include a propulsion system650, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 650 may be connected to a drive train of the vehicle600, which may include a transmission, to enable the propulsion of thevehicle 600. The propulsion system 650 may be controlled in response toreceiving signals from the throttle/accelerator 652.

A steering system 654, which may include a steering wheel, may be usedto steer the vehicle 600 (e.g., along a desired path or route) when thepropulsion system 650 is operating (e.g., when the vehicle is inmotion). The steering system 654 may receive signals from a steeringactuator 656. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 646 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 648 and/or brakesensors.

Controller(s) 636, which may include one or more system on chips (SoCs)604 (FIG. 6C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle600. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 648, to operate thesteering system 654 via one or more steering actuators 656, to operatethe propulsion system 650 via one or more throttle/accelerators 652. Thecontroller(s) 636 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 600. The controller(s) 636 may include a first controller 636for autonomous driving functions, a second controller 636 for functionalsafety functions, a third controller 636 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 636 forinfotainment functionality, a fifth controller 636 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 636 may handle two or more of the abovefunctionalities, two or more controllers 636 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 636 may provide the signals for controlling one ormore components and/or systems of the vehicle 600 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 658 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 660, ultrasonic sensor(s) 662, LIDARsensor(s) 664, inertial measurement unit (IMU) sensor(s) 666 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 696, stereo camera(s) 668, wide-view camera(s) 670(e.g., fisheye cameras), infrared camera(s) 672, surround camera(s) 674(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 698,speed sensor(s) 644 (e.g., for measuring the speed of the vehicle 600),vibration sensor(s) 642, steering sensor(s) 640, brake sensor(s) (e.g.,as part of the brake sensor system 646), and/or other sensor types.

One or more of the controller(s) 636 may receive inputs (e.g.,represented by input data) from an instrument cluster 632 of the vehicle600 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 634, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle600. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 622 of FIG. 6C), location data(e.g., the vehicle's 600 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 636,etc. For example, the HMI display 634 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 600 further includes a network interface 624 which may useone or more wireless antenna(s) 626 and/or modem(s) to communicate overone or more networks. For example, the network interface 624 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 626 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 6B is an example of camera locations and fields of view for theexample autonomous vehicle 600 of FIG. 6A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle600.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 600. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 600 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 636 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (LDW), Autonomous Cruise Control(ACC), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 670 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.6B, there may any number of wide-view cameras 670 on the vehicle 600. Inaddition, long-range camera(s) 698 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 698 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 668 may also be included in a front-facingconfiguration. The stereo camera(s) 668 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 668 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 668 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 600 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 674 (e.g., four surround cameras 674 asillustrated in FIG. 6B) may be positioned to on the vehicle 600. Thesurround camera(s) 674 may include wide-view camera(s) 670, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 674 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 600 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 698,stereo camera(s) 668), infrared camera(s) 672, etc.), as describedherein.

FIG. 6C is a block diagram of an example system architecture for theexample autonomous vehicle 600 of FIG. 6A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 600 in FIG.6C are illustrated as being connected via bus 602. The bus 602 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 600 used to aid in control of various features and functionalityof the vehicle 600, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 602 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 602, this is notintended to be limiting. For example, there may be any number of busses602, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses602 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 602 may be used for collisionavoidance functionality and a second bus 602 may be used for actuationcontrol. In any example, each bus 602 may communicate with any of thecomponents of the vehicle 600, and two or more busses 602 maycommunicate with the same components. In some examples, each SoC 604,each controller 636, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle600), and may be connected to a common bus, such the CAN bus.

The vehicle 600 may include one or more controller(s) 636, such as thosedescribed herein with respect to FIG. 6A. The controller(s) 636 may beused for a variety of functions. The controller(s) 636 may be coupled toany of the various other components and systems of the vehicle 600, andmay be used for control of the vehicle 600, artificial intelligence ofthe vehicle 600, infotainment for the vehicle 600, and/or the like.

The vehicle 600 may include a system(s) on a chip (SoC) 604. The SoC 604may include CPU(s) 606, GPU(s) 608, processor(s) 610, cache(s) 612,accelerator(s) 614, data store(s) 616, and/or other components andfeatures not illustrated. The SoC(s) 604 may be used to control thevehicle 600 in a variety of platforms and systems. For example, theSoC(s) 604 may be combined in a system (e.g., the system of the vehicle600) with an HD map 622 which may obtain map refreshes and/or updatesvia a network interface 624 from one or more servers (e.g., server(s)678 of FIG. 6D).

The CPU(s) 606 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 606 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 606may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 606 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 606 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)606 to be active at any given time.

The CPU(s) 606 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 606may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 608 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 608 may be programmable and may beefficient for parallel workloads. The GPU(s) 608, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 608 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 608 may include at least eight streamingmicroprocessors. The GPU(s) 608 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 608 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 608 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 608 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 608 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 608 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 608 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 608 to access the CPU(s) 606 page tables directly. Insuch examples, when the GPU(s) 608 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 606. In response, the CPU(s) 606 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 608. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 606 and the GPU(s) 608, thereby simplifying the GPU(s) 608programming and porting of applications to the GPU(s) 608.

In addition, the GPU(s) 608 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 608 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 604 may include any number of cache(s) 612, including thosedescribed herein. For example, the cache(s) 612 may include an L3 cachethat is available to both the CPU(s) 606 and the GPU(s) 608 (e.g., thatis connected both the CPU(s) 606 and the GPU(s) 608). The cache(s) 612may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 604 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 600—such as processingDNNs. In addition, the SoC(s) 604 may include a floating point unit(s)(FPU(s))—or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 606 and/or GPU(s) 608.

The SoC(s) 604 may include one or more accelerators 614 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 604 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 608 and to off-load some of the tasks of theGPU(s) 608 (e.g., to free up more cycles of the GPU(s) 608 forperforming other tasks). As an example, the accelerator(s) 614 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 608, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 608 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 608 and/or other accelerator(s) 614.

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 606. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 614. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 604 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 614 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 666 output thatcorrelates with the vehicle 600 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 664 or RADAR sensor(s) 660), amongothers.

The SoC(s) 604 may include data store(s) 616 (e.g., memory). The datastore(s) 616 may be on-chip memory of the SoC(s) 604, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 616 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 612 may comprise L2 or L3 cache(s) 612. Reference to thedata store(s) 616 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 614, as described herein.

The SoC(s) 604 may include one or more processor(s) 610 (e.g., embeddedprocessors). The processor(s) 610 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 604 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 604 thermals and temperature sensors, and/ormanagement of the SoC(s) 604 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 604 may use thering-oscillators to detect temperatures of the CPU(s) 606, GPU(s) 608,and/or accelerator(s) 614. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 604 into a lower powerstate and/or put the vehicle 600 into a chauffeur to safe stop mode(e.g., bring the vehicle 600 to a safe stop).

The processor(s) 610 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 610 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 610 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 610 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 610 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 610 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)670, surround camera(s) 674, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 608 is not required tocontinuously render new surfaces. Even when the GPU(s) 608 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 608 to improve performance and responsiveness.

The SoC(s) 604 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 604 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 604 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 604 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 664, RADAR sensor(s) 660,etc. that may be connected over Ethernet), data from bus 602 (e.g.,speed of vehicle 600, steering wheel position, etc.), data from GNSSsensor(s) 658 (e.g., connected over Ethernet or CAN bus). The SoC(s) 604may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 606 from routine data management tasks.

The SoC(s) 604 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 604 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 614, when combined with the CPU(s) 606, the GPU(s) 608,and the data store(s) 616, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 620) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 608.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 600. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 604 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 696 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 604 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)658. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 662, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 618 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 604 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 618 may include an X86 processor,for example. The CPU(s) 618 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 604, and/or monitoring the statusand health of the controller(s) 636 and/or infotainment SoC 630, forexample.

The vehicle 600 may include a GPU(s) 620 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 604 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 620 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 600.

The vehicle 600 may further include the network interface 624 which mayinclude one or more wireless antennas 626 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 624 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 678 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 600information about vehicles in proximity to the vehicle 600 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 600).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 600.

The network interface 624 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 636 tocommunicate over wireless networks. The network interface 624 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 600 may further include data store(s) 628 which may includeoff-chip (e.g., off the SoC(s) 604) storage. The data store(s) 628 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 600 may further include GNSS sensor(s) 658. The GNSSsensor(s) 658 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS)sensors, etc.), to assist in mapping, perception, occupancy gridgeneration, and/or path planning functions. Any number of GNSS sensor(s)658 may be used, including, for example and without limitation, a GPSusing a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle 600 may further include RADAR sensor(s) 660. The RADARsensor(s) 660 may be used by the vehicle 600 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 660 may usethe CAN and/or the bus 602 (e.g., to transmit data generated by theRADAR sensor(s) 660) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 660 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 660 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 660may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 600 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 600 lane.

Mid-range RADAR systems may include, as an example, a range of up to 660m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 650 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 600 may further include ultrasonic sensor(s) 662. Theultrasonic sensor(s) 662, which may be positioned at the front, back,and/or the sides of the vehicle 600, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 662 may be used, and different ultrasonic sensor(s) 662 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 662 may operate at functional safety levels of ASILB.

The vehicle 600 may include LIDAR sensor(s) 664. The LIDAR sensor(s) 664may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 664 maybe functional safety level ASIL B. In some examples, the vehicle 600 mayinclude multiple LIDAR sensors 664 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 664 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 664 may have an advertised rangeof approximately 600 m, with an accuracy of 2 cm-3 cm, and with supportfor a 600 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 664 may be used. In such examples,the LIDAR sensor(s) 664 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 600.The LIDAR sensor(s) 664, in such examples, may provide up to a120-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)664 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 600. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)664 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 666. The IMU sensor(s) 666may be located at a center of the rear axle of the vehicle 600, in someexamples. The IMU sensor(s) 666 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 666 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 666 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 666 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 666 may enable the vehicle 600to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 666. In some examples, the IMU sensor(s) 666 and theGNSS sensor(s) 658 may be combined in a single integrated unit.

The vehicle may include microphone(s) 696 placed in and/or around thevehicle 600. The microphone(s) 696 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 668, wide-view camera(s) 670, infrared camera(s) 672,surround camera(s) 674, long-range and/or mid-range camera(s) 698,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 600. The types of cameras useddepends on the embodiments and requirements for the vehicle 600, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 600. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 6A and FIG. 6B.

The vehicle 600 may further include vibration sensor(s) 642. Thevibration sensor(s) 642 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 642 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 600 may include an ADAS system 638. The ADAS system 638 mayinclude a SoC, in some examples. The ADAS system 638 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (B S W), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 660, LIDAR sensor(s) 664, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 600 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 600 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 624 and/or the wireless antenna(s) 626 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 600), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 600, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 660, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 660, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle600 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 600 if the vehicle 600 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)660, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 600 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 660, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 600, the vehicle 600itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 636 or a second controller 636). For example, in someembodiments, the ADAS system 638 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 638may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 604.

In other examples, ADAS system 638 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 638 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 638indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 600 may further include the infotainment SoC 630 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 630 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 600. For example, the infotainment SoC 630 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 634, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 630 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 638,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 630 may include GPU functionality. The infotainmentSoC 630 may communicate over the bus 602 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 600. Insome examples, the infotainment SoC 630 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 636(e.g., the primary and/or backup computers of the vehicle 600) fail. Insuch an example, the infotainment SoC 630 may put the vehicle 600 into achauffeur to safe stop mode, as described herein.

The vehicle 600 may further include an instrument cluster 632 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 632 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 632 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 630 and theinstrument cluster 632. In other words, the instrument cluster 632 maybe included as part of the infotainment SoC 630, or vice versa.

FIG. 6D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 600 of FIG. 6A, inaccordance with some embodiments of the present disclosure. The system676 may include server(s) 678, network(s) 690, and vehicles, includingthe vehicle 600. The server(s) 678 may include a plurality of GPUs684(A)-684(H) (collectively referred to herein as GPUs 684), PCIeswitches 682(A)-682(H) (collectively referred to herein as PCIe switches682), and/or CPUs 680(A)-680(B) (collectively referred to herein as CPUs680). The GPUs 684, the CPUs 680, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 688 developed by NVIDIA and/orPCIe connections 686. In some examples, the GPUs 684 are connected viaNVLink and/or NVSwitch SoC and the GPUs 684 and the PCIe switches 682are connected via PCIe interconnects. Although eight GPUs 684, two CPUs680, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 678 mayinclude any number of GPUs 684, CPUs 680, and/or PCIe switches. Forexample, the server(s) 678 may each include eight, sixteen, thirty-two,and/or more GPUs 684.

The server(s) 678 may receive, over the network(s) 690 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 678 may transmit, over the network(s) 690 and to the vehicles,neural networks 692, updated neural networks 692, and/or map information694, including information regarding traffic and road conditions. Theupdates to the map information 694 may include updates for the HD map622, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 692, the updated neural networks 692, and/or the mapinformation 694 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 678 and/or other servers).

The server(s) 678 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training,self-learning, reinforcement learning, federated learning, transferlearning, feature learning (including principal component and clusteranalyses), multi-linear subspace learning, manifold learning,representation learning (including spare dictionary learning),rule-based machine learning, anomaly detection, and any variants orcombinations therefor. Once the machine learning models are trained, themachine learning models may be used by the vehicles (e.g., transmittedto the vehicles over the network(s) 690, and/or the machine learningmodels may be used by the server(s) 678 to remotely monitor thevehicles.

In some examples, the server(s) 678 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 678 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 684, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 678 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 678 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 600. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 600, suchas a sequence of images and/or objects that the vehicle 600 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 600 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 600 is malfunctioning, the server(s) 678 may transmit asignal to the vehicle 600 instructing a fail-safe computer of thevehicle 600 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 678 may include the GPU(s) 684 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 7 is a block diagram of an example computing device(s) 700 suitablefor use in implementing some embodiments of the present disclosure.Computing device 700 may include an interconnect system 702 thatdirectly or indirectly couples the following devices: memory 704, one ormore central processing units (CPUs) 706, one or more graphicsprocessing units (GPUs) 708, a communication interface 710, input/output(I/O) ports 712, input/output components 714, a power supply 716, one ormore presentation components 718 (e.g., display(s)), and one or morelogic units 720. In at least one embodiment, the computing device(s) 700may comprise one or more virtual machines (VMs), and/or any of thecomponents thereof may comprise virtual components (e.g., virtualhardware components). For non-limiting examples, one or more of the GPUs708 may comprise one or more vGPUs, one or more of the CPUs 706 maycomprise one or more vCPUs, and/or one or more of the logic units 720may comprise one or more virtual logic units. As such, a computingdevice(s) 700 may include discrete components (e.g., a full GPUdedicated to the computing device 700), virtual components (e.g., aportion of a GPU dedicated to the computing device 700), or acombination thereof.

Although the various blocks of FIG. 7 are shown as connected via theinterconnect system 702 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 718, such as a display device, may be consideredan I/O component 714 (e.g., if the display is a touch screen). Asanother example, the CPUs 706 and/or GPUs 708 may include memory (e.g.,the memory 704 may be representative of a storage device in addition tothe memory of the GPUs 708, the CPUs 706, and/or other components). Inother words, the computing device of FIG. 7 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.7 .

The interconnect system 702 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 702 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 706 may be directly connectedto the memory 704. Further, the CPU 706 may be directly connected to theGPU 708. Where there is direct, or point-to-point connection betweencomponents, the interconnect system 702 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 700.

The memory 704 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 700. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 704 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device700. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 706 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 700 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 706 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 706 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 700 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 700, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 700 mayinclude one or more CPUs 706 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 706, the GPU(s) 708 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device700 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 708 may be an integrated GPU (e.g.,with one or more of the CPU(s) 706 and/or one or more of the GPU(s) 708may be a discrete GPU. In embodiments, one or more of the GPU(s) 708 maybe a coprocessor of one or more of the CPU(s) 706. The GPU(s) 708 may beused by the computing device 700 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 708 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 708may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 708may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 706 received via ahost interface). The GPU(s) 708 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory704. The GPU(s) 708 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 708 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 706 and/or the GPU(s)708, the logic unit(s) 720 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 700 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 706, the GPU(s)708, and/or the logic unit(s) 720 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 720 may be part of and/or integrated in one ormore of the CPU(s) 706 and/or the GPU(s) 708 and/or one or more of thelogic units 720 may be discrete components or otherwise external to theCPU(s) 706 and/or the GPU(s) 708. In embodiments, one or more of thelogic units 720 may be a coprocessor of one or more of the CPU(s) 706and/or one or more of the GPU(s) 708.

Examples of the logic unit(s) 720 include one or more processing coresand/or components thereof, such as Data Processing Units (DPUs), TensorCores (TCs), Tensor Processing Units (TPUs), Pixel Visual Cores (PVCs),Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs),Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs),Tree Traversal Units (TTUs), Artificial Intelligence Accelerators(AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units(ALUs), Application-Specific Integrated Circuits (ASICs), Floating PointUnits (FPUs), input/output (I/O) elements, peripheral componentinterconnect (PCI) or peripheral component interconnect express (PCIe)elements, and/or the like.

The communication interface 710 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 700to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 710 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet. In one or more embodiments, logic unit(s) 720and/or communication interface 710 may include one or more dataprocessing units (DPUs) to transmit data received over a network and/orthrough interconnect system 702 directly to (e.g., a memory of) one ormore GPU(s) 708.

The I/O ports 712 may enable the computing device 700 to be logicallycoupled to other devices including the I/O components 714, thepresentation component(s) 718, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 700.Illustrative I/O components 714 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 714 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 700. Thecomputing device 700 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 700 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 700 to render immersive augmented reality or virtual reality.

The power supply 716 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 716 may providepower to the computing device 700 to enable the components of thecomputing device 700 to operate.

The presentation component(s) 718 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 718 may receivedata from other components (e.g., the GPU(s) 708, the CPU(s) 706, DPUs,etc.), and output the data (e.g., as an image, video, sound, etc.).

Example Data Center

FIG. 8 illustrates an example data center 800 that may be used in atleast one embodiments of the present disclosure. The data center 800 mayinclude a data center infrastructure layer 810, a framework layer 820, asoftware layer 830, and/or an application layer 840.

As shown in FIG. 8 , the data center infrastructure layer 810 mayinclude a resource orchestrator 812, grouped computing resources 814,and node computing resources (“node C.R.s”) 816(1)-816(N), where “N”represents any whole, positive integer. In at least one embodiment, nodeC.R.s 816(1)-816(N) may include, but are not limited to, any number ofcentral processing units (CPUs) or other processors (including DPUs,accelerators, field programmable gate arrays (FPGAs), graphicsprocessors or graphics processing units (GPUs), etc.), memory devices(e.g., dynamic read-only memory), storage devices (e.g., solid state ordisk drives), network input/output (NW I/O) devices, network switches,virtual machines (VMs), power modules, and/or cooling modules, etc. Insome embodiments, one or more node C.R.s from among node C.R.s816(1)-816(N) may correspond to a server having one or more of theabove-mentioned computing resources. In addition, in some embodiments,the node C.R.s 816(1)-8161(N) may include one or more virtualcomponents, such as vGPUs, vCPUs, and/or the like, and/or one or more ofthe node C.R.s 816(1)-816(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources 814 may includeseparate groupings of node C.R.s 816 housed within one or more racks(not shown), or many racks housed in data centers at variousgeographical locations (also not shown). Separate groupings of nodeC.R.s 816 within grouped computing resources 814 may include groupedcompute, network, memory or storage resources that may be configured orallocated to support one or more workloads. In at least one embodiment,several node C.R.s 816 including CPUs, GPUs, DPUs, and/or otherprocessors may be grouped within one or more racks to provide computeresources to support one or more workloads. The one or more racks mayalso include any number of power modules, cooling modules, and/ornetwork switches, in any combination.

The resource orchestrator 812 may configure or otherwise control one ormore node C.R.s 816(1)-816(N) and/or grouped computing resources 814. Inat least one embodiment, resource orchestrator 812 may include asoftware design infrastructure (SDI) management entity for the datacenter 800. The resource orchestrator 812 may include hardware,software, or some combination thereof.

In at least one embodiment, as shown in FIG. 8 , framework layer 820 mayinclude a job scheduler 833, a configuration manager 834, a resourcemanager 836, and/or a distributed file system 838. The framework layer820 may include a framework to support software 832 of software layer830 and/or one or more application(s) 842 of application layer 840. Thesoftware 832 or application(s) 842 may respectively include web-basedservice software or applications, such as those provided by Amazon WebServices, Google Cloud and Microsoft Azure. The framework layer 820 maybe, but is not limited to, a type of free and open-source software webapplication framework such as Apache Spark™ (hereinafter “Spark”) thatmay utilize distributed file system 838 for large-scale data processing(e.g., “big data”). In at least one embodiment, job scheduler 833 mayinclude a Spark driver to facilitate scheduling of workloads supportedby various layers of data center 800. The configuration manager 834 maybe capable of configuring different layers such as software layer 830and framework layer 820 including Spark and distributed file system 838for supporting large-scale data processing. The resource manager 836 maybe capable of managing clustered or grouped computing resources mappedto or allocated for support of distributed file system 838 and jobscheduler 833. In at least one embodiment, clustered or groupedcomputing resources may include grouped computing resource 814 at datacenter infrastructure layer 810. The resource manager 836 may coordinatewith resource orchestrator 812 to manage these mapped or allocatedcomputing resources.

In at least one embodiment, software 832 included in software layer 830may include software used by at least portions of node C.R.s816(1)-816(N), grouped computing resources 814, and/or distributed filesystem 838 of framework layer 820. One or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 842 included in applicationlayer 840 may include one or more types of applications used by at leastportions of node C.R.s 816(1)-816(N), grouped computing resources 814,and/or distributed file system 838 of framework layer 820. One or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.),and/or other machine learning applications used in conjunction with oneor more embodiments.

In at least one embodiment, any of configuration manager 834, resourcemanager 836, and resource orchestrator 812 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. Self-modifying actions mayrelieve a data center operator of data center 800 from making possiblybad configuration decisions and possibly avoiding underutilized and/orpoor performing portions of a data center.

The data center 800 may include tools, services, software or otherresources to train one or more machine learning models or predict orinfer information using one or more machine learning models according toone or more embodiments described herein. For example, a machinelearning model(s) may be trained by calculating weight parametersaccording to a neural network architecture using software and/orcomputing resources described above with respect to the data center 800.In at least one embodiment, trained or deployed machine learning modelscorresponding to one or more neural networks may be used to infer orpredict information using resources described above with respect to thedata center 800 by using weight parameters calculated through one ormore training techniques, such as but not limited to those describedherein.

In at least one embodiment, the data center 800 may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, and/orother hardware (or virtual compute resources corresponding thereto) toperform training and/or inferencing using above-described resources.Moreover, one or more software and/or hardware resources described abovemay be configured as a service to allow users to train or performinginferencing of information, such as image recognition, speechrecognition, or other artificial intelligence services.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of thecomputing device(s) 700 of FIG. 7 —e.g., each device may include similarcomponents, features, and/or functionality of the computing device(s)700. In addition, where backend devices (e.g., servers, NAS, etc.) areimplemented, the backend devices may be included as part of a datacenter 800, an example of which is described in more detail herein withrespect to FIG. 8 .

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example computing device(s) 700described herein with respect to FIG. 7 . By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A processor comprising: one or more circuits to:compute, using a neural network and based at least on image data of anenvironment generated using one or more image sensors, datarepresentative of one or more parameters of an operational domaincorresponding to the environment; determine, based at least in part onthe data, an operational level of an ego-machine corresponding to theenvironment; and control an operation of the ego-machine according tothe operational level.
 2. The processor of claim 1, wherein the one ormore parameters comprise at least one of: a camera blindness level; ablindness classification; an illumination level; a path surfacecondition; a visibility distance; a scene type classification; or adistance to a scene corresponding to a scene type classification.
 3. Theprocessor of claim 1 comprising one or more circuits to: generateintermediate data using a trunk of the neural network; compute a subsetof the data based on the intermediate data and using at least one headof the neural network, wherein the subset of the data is representativeof at least one of: a camera blindness level; a blindnessclassification; an illumination level; a path surface condition; avisibility distance; a scene type classification; or a distance to ascene corresponding to a scene type classification.
 4. The processor ofclaim 3, wherein the at least one head includes one or moredown-sampling layers followed by one or more up-sampling layers.
 5. Theprocessor of claim 3, wherein the trunk includes one or moreconvolutional layers, and the intermediate data is representative of oneor more feature maps.
 6. The processor of claim 3, wherein the at leastone head includes at least one fully connected layer.
 7. The processorof claim 3, wherein the at least one head includes a sigmoid function.8. The processor of claim 2, wherein the determination of theoperational level of the ego-machine includes weighting at least one ofthe camera blindness level, the blindness classification, theillumination level, the path surface condition, the visibility distance,the scene type classification, or the distance to a scene according topre-defined weights.
 9. The processor of claim 8, wherein the camerablindness level has a highest weight of the pre-defined weights.
 10. Theprocessor of claim 1, wherein the operational level corresponds tolevels of vehicle autonomy including level 0 (L0), level 1 (L1), level 2(L2), level 3 (L3), level 4 (L4), or level 5 (L5).
 11. The processor ofclaim 2, where the scene type classification includes at least one oftunnels, construction zones, toll plazas, or blocked lanes.
 12. Theprocessor of claim 1, wherein the data includes a camera-based input tothe determination of the operational level, and the determination of theoperational level is further based at least in part on data generatedusing one or more other sensor modalities.
 13. The processor of claim 1,wherein the processor is comprised in at least one of: a control systemfor an autonomous or semi-autonomous machine; a perception system for anautonomous or semi-autonomous machine; a system for performingsimulation operations; a system for performing deep learning operations;a system implemented using an edge device; a system implemented using arobot; a system incorporating one or more virtual machines (VMs); asystem implemented at least partially in a data center; or a systemimplemented at least partially using cloud computing resources.
 14. Asystem comprising: one or more image sensors to generate image data; oneor more processing units to: compute, using a neural network and basedat least in part on the image data, data representative of at least oneof an illumination level, a path surface condition, a visibilitydistance, at least one of a scene type classification or a distance to ascene corresponding to the scene type classification, and at least oneof an amount of camera blindness or a source of camera blindness;determine an operational level of the ego-machine based at least in parton the data; and determine one or more control operations based at leastin part on the operational level.
 15. The system of claim 14, whereinthe data includes a camera-based input to the determination of theoperational level, and the determination of the operational level isfurther based at least in part on data generated using one or more othersensor modalities.
 16. The system of claim 14, wherein the determinationof the operational level of the ego-machine includes weighting theillumination level, the path surface condition, the visibility distance,at least one of the scene type classification or the distance to thescene corresponding to the scene type classification, and at least oneof the amount of camera blindness or the source camera blindnessaccording to pre-defined weights.
 17. The system of claim 16, whereinthe amount of camera blindness has a highest weight of the pre-definedweights.
 18. The system of claim 14, wherein the system is comprised inat least one of: a control system for an autonomous or semi-autonomousmachine; a perception system for an autonomous or semi-autonomousmachine; a system for performing simulation operations; a system forperforming deep learning operations; a system implemented using an edgedevice; a system implemented using a robot; a system incorporating oneor more virtual machines (VMs); a system implemented at least partiallyin a data center; or a system implemented at least partially using cloudcomputing resources.
 19. A processor comprising: processing circuitry todetermine an operational level of an ego-machine based at least in parton data computed using a neural network and based at least in part onthe neural network processing image data generated using one or moreimage sensors of the ego-machine, the data representative of at leastone of an illumination level, a path surface condition, a visibilitydistance, at least one of a scene type classification or a distance to ascene corresponding to the scene type classification, and at least oneof an amount of camera blindness or a source of camera blindness. 20.The processor of claim 19, wherein the data includes a camera-basedinput to the determination of the operational level, and thedetermination of the operational level is further based at least in parton data generated using one or more other sensor modalities.